---
title: Deep-Learning–Based Prediction of c-MET Status From Digitized H&E-Stained Non-small Cell Lung Cancer Tissue Samples
description: Deep-Learning–Based Prediction of c-MET Status From Digitized H&E-Stained Non-small Cell Lung Cancer Tissue Samples
---

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# [Deep-Learning–Based Prediction of c-MET Status From Digitized H&E-Stained Non-small Cell Lung Cancer Tissue Samples](https://www.pathai.com/resources/deep-learning-based-prediction-of-c-met-status-from-digitized-he-stained-non-small-cell-lung-cancer-tissue-samples)

 Written by [Admin](https://www.pathai.com/resources/author/admin) | Aug 6, 2022 4:00:00 AM

## Study Background

 The human-interpretable features (HIF) generated by the convolutional neural networks (CNN) models showed that the composition of the tumor microenvironment (TME) is significantly different in tissue, including lymphocyte density, when c-MET is overexpressed.

Multivariate regression (MR) based on TME features and graph neural networks (GNN) models was able to identify patients with tumors that overexpressed c-MET; however, the performance of the GNN model was stronger.

These results indicate there is a potential to develop and validate an H&E-based screening tool for patient selection for c-MET targeting therapies, thereby increasing efficiency and access. Rajan et al.

[View Poster](https://www.pathai.com/hubfs/2025%20Assets/Updated_WCLC22_Predictionc-MET_PathAI.pdf)

[View full post](https://www.pathai.com/resources/deep-learning-based-prediction-of-c-met-status-from-digitized-he-stained-non-small-cell-lung-cancer-tissue-samples)

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